Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Classification of Signals01:30

Classification of Signals

1.3K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.3K
Stimulants01:29

Stimulants

807
Stimulants are substances that enhance neural activity and elevate dopamine levels in the brain, leading to their highly addictive nature. These drugs include cocaine, amphetamines, MDMA, caffeine, and nicotine, each with distinct mechanisms of action and varied health implications.
Cocaine can be administered via snorting, injection, or smoking. It primarily functions by blocking the reuptake of dopamine, resulting in a euphoric high characterized by an intense sensation of happiness and...
807
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

42.5K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
42.5K
Classification of Illness01:17

Classification of Illness

8.5K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.5K
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

4.9K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
4.9K
Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

1.5K
Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...
1.5K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Emotional self-regulation, impulsivity, 5-HTTLPR and tobacco use behavior among psychiatric inpatients.

Journal of affective disorders·2022
Same author

Transgenesis in parasitic helminths: a brief history and prospects for the future.

Parasites & vectors·2022
Same author

Using the EM algorithm for Bayesian variable selection in logistic regression models with related covariates.

Journal of statistical computation and simulation·2018
Same author

Flavored e-cigarette use: Characterizing youth, young adult, and adult users.

Preventive medicine reports·2016
Same author

Identifying the immunomodulatory components of helminths.

Parasite immunology·2015
Same author

Immunization and challenge shown by hamsters infected with Opisthorchis viverrini following exposure to gamma-irradiated metacercariae of this carcinogenic liver fluke.

Journal of helminthology·2014
Same journal

Profiling the vendors of COVID-19 related product on the Darknet: An observational study.

Emerging trends in drugs, addictions, and health·2023
Same journal

Tramadol in seized drugs containing non-pharmaceutical fentanyl: Crime lab data from Ohio, USA.

Emerging trends in drugs, addictions, and health·2023
Same journal

Online 12-step groups during the Covid-19 pandemic: A patient's perspective.

Emerging trends in drugs, addictions, and health·2022
Same journal

Gambling at the time of COVID-19: Results from interviews in an Italian sample of gamblers.

Emerging trends in drugs, addictions, and health·2022
Same journal

Psychological states of Bangladeshi people and associated factors during the outbreak of COVID-19: A cross-sectional survey.

Emerging trends in drugs, addictions, and health·2021
Same journal

A Strategy to Prioritize Emerging Drugs of Abuse for Analysis: Abuse Liability Testing Using Intracranial Self-Stimulation (ICSS) in Rats and Validation with α-Pyrrolidinohexanophenone (α-PHP).

Emerging trends in drugs, addictions, and health·2021
查看所有相关文章

相关实验视频

Updated: Jan 8, 2026

Generation of Electronic Cigarette Aerosol by a Third-Generation Machine-Vaping Device: Application to Toxicological Studies
08:39

Generation of Electronic Cigarette Aerosol by a Third-Generation Machine-Vaping Device: Application to Toxicological Studies

Published on: August 25, 2018

26.3K

使用BERT主题建模对电子烟相关推文进行分类.

D Murthy1, S Keshari2, S Arora3

  • 1Professor of Media Studies, Sociology, and Information, University of Texas at Austin, United States of America.

Emerging trends in drugs, addictions, and health
|December 17, 2025
PubMed
概括
此摘要是机器生成的。

机器学习有效地将超过10万条电子烟推特分为不同的主题,帮助公共卫生干预. 对社交媒体关于电子烟言论的分析为反向信息和政策制定提供了洞察力.

关键词:
贝尔托比克 (Bertopic) 是一个专题.电子烟是电子烟的产品.自然语言处理自然语言处理.主题建模 主题建模电子烟 (Vape) 是一种电子烟.

更多相关视频

Comparing the Effects of Electronic Cigarette Vapor and Cigarette Smoke in a Novel In Vivo Exposure System
10:44

Comparing the Effects of Electronic Cigarette Vapor and Cigarette Smoke in a Novel In Vivo Exposure System

Published on: May 24, 2017

12.0K
A Microcontroller Operated Device for the Generation of Liquid Extracts from Conventional Cigarette Smoke and Electronic Cigarette Aerosol
09:30

A Microcontroller Operated Device for the Generation of Liquid Extracts from Conventional Cigarette Smoke and Electronic Cigarette Aerosol

Published on: January 18, 2018

8.8K

相关实验视频

Last Updated: Jan 8, 2026

Generation of Electronic Cigarette Aerosol by a Third-Generation Machine-Vaping Device: Application to Toxicological Studies
08:39

Generation of Electronic Cigarette Aerosol by a Third-Generation Machine-Vaping Device: Application to Toxicological Studies

Published on: August 25, 2018

26.3K
Comparing the Effects of Electronic Cigarette Vapor and Cigarette Smoke in a Novel In Vivo Exposure System
10:44

Comparing the Effects of Electronic Cigarette Vapor and Cigarette Smoke in a Novel In Vivo Exposure System

Published on: May 24, 2017

12.0K
A Microcontroller Operated Device for the Generation of Liquid Extracts from Conventional Cigarette Smoke and Electronic Cigarette Aerosol
09:30

A Microcontroller Operated Device for the Generation of Liquid Extracts from Conventional Cigarette Smoke and Electronic Cigarette Aerosol

Published on: January 18, 2018

8.8K

科学领域:

  • 公共卫生 公共卫生
  • 计算社会科学 计算社会科学
  • 数字健康数字健康

背景情况:

  • 社交媒体平台是电子烟推广的关键道,特别是在年轻人中.
  • 分析各种社交媒体内容对于有关电子烟使用的公共卫生干预至关重要.
  • 传统的内容分析方法是劳动密集型的,对于大型数据集缺乏可扩展性.

研究的目的:

  • 评估机器学习,特别是主题建模在对电子烟相关推特进行分类方面的有效性.
  • 在社交媒体上关于电子烟的讨论中确定关键主题和模式.
  • 通过更好地理解在线话语,为公共卫生反向信息和政策干预提供信息.

主要方法:

  • 使用BERTopic建模来导出和集群与电子烟相关的推文.
  • 对聚类推文进行了定性内容分析,以获得主题理解.
  • 采用自动化地理分析来推断电子烟对话的地理位置.

主要成果:

  • 成功识别了超过10万条推特,分为英语和西班牙语的不同主题.
  • 确定了六个主要主题:风味/一次性蒸汽,大麻,vape商店/充电式蒸汽,vape文化,反vaping/戒烟,以及西班牙推文/尼古丁蒸汽.
  • 地理分析表明,美国是拥有最高数量的电子烟相关推特的地区.

结论:

  • 机器学习,特别是BERTopic,可以有效地减少和分类大量关于电子烟的社交媒体数据.
  • 确定的主题为不断发展的电子烟话语提供了全面的理解.
  • 调查结果支持监管的必要性 (例如,风味限制),并强调社交媒体对公共卫生信息的潜力,例如戒烟运动.